This patent application extends the usefulness of the following two prior patents, the disclosures of which are hereby incorporated by reference in their entirety, as if fully set forth herein:                U.S. Pat. No. 9,053,636 Management Center Module for Advanced Lane Management Assist for Automated Vehicles and Conventionally Driven Vehicles (ALMAMC)        U.S. Pat. No. 9,286,800 Guidance Assist Vehicle Module (ALMAVM)        
These patents describe a methodology (ALMA) for using traffic management center (TMC) information to select a most appropriate freeway lane for a driver or automated vehicle and to provide a target speed for that lane. The TMC traffic condition information, is essentially current information on traffic speed and other variables for each through traffic lane. The information is organized according to a data structure described in in the ALMAMC patent that considers the physical and functional features of the freeway as well as traffic information devices. The information is transmitted to the vehicle where it is further processed (ALMAVM). This additional processing develops guidance on the best lane and target speed by looking at traffic speeds for several miles ahead (downstream) of the vehicle's current position.
Since the vehicle may not reach the look-ahead distance for a few minutes, the current patent improves the performance of the prior patents by using predicted traffic speed in place of current traffic speed for lane selection and target speed recommendations. To obtain lane based speed information, TMCs may use sources such as roadway based traffic detectors and reports from connected vehicles that include position, speed and lane identification. Prediction for other key parameters provided by the ALMAMC patent is provided.
Pan et al1 provide a review of traffic prediction techniques. Examples of prediction techniques include: 1BEI PAN, UGUR DEMIRYUREK, and CYRUS SHAHABI, Utilizing Real-World Transportation Data for Accurate Traffic Prediction, Integrated Media System Center, University of Southern California.                1. Simulation Models—Traffic prediction using microscopic simulation models in conjunction with traffic detector measurements. References include Gehrke and J. Wojtusiak2 and Ben Akiva et al3. 2 JAN D. GEHRKE and JANUSZ WOJTUSIAK, A Natural Induction Approach to Traffic Prediction for Autonomous Agent-based Vehicle Route Planning, Machine Learning and Inference Laboratory, MLI 08-1, George Mason University, Feb. 17, 2008.3 MOSHE BEN-AKIVA, MICHEL BIERLAIRE, HARTS KOUTSOPOULOS, and RABI MISHALANI, DynaMIT: a simulation-based system for traffic prediction, Massachusetts Institute of Technology Intelligent Transportation Systems Program, Presented Paper presented at the DACCORD Short Term Forecasting Workshop February, 1998.        2. Data Mining Techniques—This class analyzes the data collected by traffic detectors. Various analysis approaches include:                    A. Auto-Regressive Integrated Moving Average (ARIMA) Model.4 The Exponential Smoothing model is a special case of ARIMA that has been extensively used for traffic data applications. Strictly speaking these models are just estimators of current conditions that are used as predictors. Although not discussed by Pan, The ALMAMC software uses a Kalman Filter in this manner to process lane specific information developed by the TMC from traffic detectors and other sources. 4 G. BOX and G. JENKINS, Time series analysis: Forecasting and control. San Francisco: Holden-Day, 1970 (book).            B. Neural Network Models have been used for traffic prediction5 as have genetic algorithms. 5 SHERIF ISHAK and CIPRIAN ALECSANDRU, Optimizing Traffic Prediction Performance of Neural Networks under Various Topological, Input, and Traffic Condition Settings, JTE′04, Volume 130.            C. Historical Models—These models process historical data and provide prediction by reference to a future time period.                        
Pan concludes that he ARIMA/Exponential Smoothing models are best for short term prediction (our interest) and that historical models are best for long term prediction. Pan provides an algorithm based on error characteristics to select between them.